Data fusion can be generally defined as the use of techniques that combine data from multiple sources in order to achieve situational assessments or inferences about that data. A data fusion system may, for example, employ a stochastic model for performing the data fusion. Different stochastic models can be used for performing data fusion, including, for example, Hidden Markov Models, Bayesian Networks, and Neural Networks.
Data fusion can be utilized in a wide variety of applications, including both military and non-military. Examples of such applications include: territorial security, monitoring of manufacturing processes, environmental monitoring, command and control, business intelligence, emergency management, and medical applications. For example, in a territorial security system, a plurality of sensors can be positioned, perhaps over a large area, and used to monitor activity in the area. The data from each of the sensors is processed by a data fusion system to make an inference of whether an activity monitored by the sensors warrants taking further action, such as alerting a human security authority.
In applications such as territorial security, it may be necessary for a data fusion system to be able to operate in real-time so that proper inferences can be made and appropriate action can be taken in a suitable amount of time. However, some data fusion systems are too computationally intense to be optimal for territorial security related applications.
Other data fusion systems, which may be suitable for territorial security related applications, must first be trained offline during a training phase using a set of training data consisting of known inputs. The training allows for the generation of model parameters of the data fusion system. Once trained, during subsequent operation in real-time, the data fusion system can receive actual “live” data and make inferences in accordance with established model parameters. However, these model parameters are typically not updated during operation subsequent to the training phase. Rather, if it is desired to update the model parameters at a later time, for example, because the performance of the data fusion system becomes suboptimal due to a growing discrepancy between the training data and the actual input data during operation, the data fusion system must be taken out of operation, and the data fusion system must be re-trained to establish new model parameters.